#approximationtheory
Live, measured metrics for the hashtag #approximationtheory from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-08-01 15:35 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-08-01 15:35 UTCNo related tags with measured usage found for #approximationtheory.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-08-01 15:35 UTCEverything below is measured over the latest 3 public posts (spanning ~13129 hours).
Posting hours (UTC)
Languages: English (3)
Avg boosts / post: 0.3
Top of the latest posts
I recently read two interesting survey articles by my academic brother Ben Adcock at Simon Fraser University about theoretical aspect of sampling: how to approximate a function 𝑓 given random point samples 𝑓(𝑥ᵢ) with noise. This is a fun
This chocolate reminded me of Bernstein ellipses, which govern how fast the Chebyshev approximation converges to a function. #MathsIsEverywhere #OccupationalHazard #ApproximationTheory
My thoughts keep turning back to the OWNA (One World Numerical Analysis) talk of Daan Huybrechs a few weeks ago. Most of numerical analysis is built on approximating functions in finite-dimensional spaces: \[ f(x) \approx \sum_i a_i \varphi
#approximationtheory across platforms
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Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/approximationtheory